Assessment tools for the cerebral cortical dysfunction secondary to Alzheimer's disease (AD) or stroke are urgently needed. With our increasingly aging population, cortical dysfunction due to AD and stroke are becoming more common. Nationwide, these represent the top two causes for cognitive impairment and loss of the ability for independent living. The cost to patients, family members, and society is staggering. The need to provide accurate diagnostic classification and staging, to assess prognostic indicators, and to have tools for reliable evaluation of the efficacy of intervention becomes critical. The proposed project will consist of two phases. Phase one will concentrate on the development, testing, and implementation of algorithms for EEG analysis. Patient data will be collected and used for testing in this phase. For the EG signal peak determination, a new model using wavelet analysis will be used, followed by measurement of the variability of the peak occurrence based on chaos theory. Chaos modeling has been shown to add a new dimension to biomedical signal analysis as illustrated in applications to ECG analysis and hemodynamic studies. The results from the EEG analysis will be combined with additional parameters obtained from cognitive testing, clinical examination, and imaging using a hybrid system to develop a classification model. The proposed comprehensive tool has the potential to be developed into a standard for evaluation of new drugs and rehabilitation strategies.